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Gene expression profiles are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. This has received most attention in tumor classification. In this paper we attempt to introduce a method combined neural networks with two feature selection mechanisms for tumor classification. Also we proposed a voting weight method to combine the classification results of two individual neural networks. Then we validate our method on two publicly available datasets. Compared with other current methods, our method greatly improves the accuracy and robustness of such classification. We hopefully expect that the biomarker genes analyzed by the method would give more instruction in biological experiments and clinical diagnosis reference.
Date of Conference: 16-18 May 2008